Guide to Connectomes

Connectomes.Connectome — Type

Connectome(path::String; norm=true)

Main type introduced by Connectomes.jl,

struct Connectome
    parc::DataFrame
    graph::SimpleWeightedGraph{Int64, Float64}
    n_matrix::Matrix{Float64}
    l_matrix::Matrix{Float64}
end

where parc is the parcellation atlas, graph is a SimpleWeightedGraph encoding a weighted Connectome, n_matrix is the length matrix and l_matrix is the length matrix.

Example

julia> filter(Connectome(Connectomes.connectomepath()), 1e-2)
Parcellation: 
83×8 DataFrame
 Row │ ID     Label                 Region       Hemisphere  x          y           z            Lobe      
     │ Int64  String                String       String      Float64    Float64     Float64      String    
─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │     1  lateralorbitofrontal  cortical     right        25.0057    33.4625    -16.6508     frontal
   2 │     2  parsorbitalis         cortical     right        43.7891    41.4659    -11.8451     frontal
   3 │     3  frontalpole           cortical     right         9.59579   67.3442     -8.94211    frontal
   4 │     4  medialorbitofrontal   cortical     right         5.799     40.7383    -15.7166     frontal
   5 │     5  parstriangularis      cortical     right        48.3993    31.8555      5.60427    frontal
  ⋮  │   ⋮             ⋮                 ⋮           ⋮           ⋮          ⋮            ⋮           ⋮
  80 │    80  Left-Accumbens-area   subcortical  left         -8.14103   11.416      -6.32051    subcortex
  81 │    81  Left-Hippocampus      subcortical  left        -25.5001   -22.6622    -13.6924     temporal
  82 │    82  Left-Amygdala         subcortical  left        -22.7183    -5.11994   -18.8364     temporal
  83 │    83  brainstem             subcortical  none         -6.07796  -31.5015    -32.8539     subcortex
                                                                                            74 rows omitted
Adjacency Matrix: 
83×83 SparseArrays.SparseMatrixCSC{Float64, Int64} with 392 stored entries:
⣮⢛⣣⡠⠀⠀⠀⠀⠀⠀⠀⡁⠀⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠉⡺⢺⠒⣒⠄⢀⠀⠀⠀⠀⠄⠀⠀⠀⠀⠂⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠘⠜⠚⣠⣐⡐⠀⠀⣀⡄⠀⠀⠀⠀⠀⠈⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠐⢐⠸⢴⡳⡄⠌⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠂⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⡀⠍⠯⡧⡄⠀⠀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠄⠠⠀⠄⠀⠼⠁⠀⠀⠉⠯⡣⣄⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠄
⠀⠀⠀⠀⠀⠀⠀⠀⠤⠄⠀⠝⠏⠅⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠀⠁
⠀⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡎⡭⡦⠂⠀⠀⠀⠀⠀⠀⠠⠀⠀⠀
⠀⠀⠈⠠⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠨⠋⡏⡩⡕⠀⠄⠀⠀⠐⠐⠀⠀⠀
⠀⠀⠀⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠉⠡⡦⢥⠁⠀⢰⠶⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠁⠅⠓⢯⣳⣐⠂⠀⠀⢀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⢀⣀⠰⠘⢺⣲⣀⠀⠘⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠐⠀⠂⠐⠀⠘⠃⠀⠀⠀⠘⢪⣲⣔⡂
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠄⠄⠀⠀⠀⠀⠀⠀⠀⠀⠐⠒⠀⠰⠹⠐⠀

source

Plotting Example

Firstly, you will need to load Connectomes and a plotting backend from the Makie. Connectomes.jl uses the Makie.jl backend to organise and render plots.

There are several plotting methods available in Connectomes.jl. In keeping with the Julia custom, plotting methods ending with a ! add to an existing plot. Whereas those without ! create a Makie Scene.

using WGLMakie
using Connectomes

plot_cortex()